bibtex.bib

@book{van2011python,
  title={The python language reference manual},
  author={Van Rossum, Guido and Drake, Fred L},
  year={2011},
  publisher={Network Theory Ltd.}
}

@article{holman2018gender,
  title={The gender gap in science: How long until women are equally represented?},
  author={Holman, Luke and Stuart-Fox, Devi and Hauser, Cindy E},
  journal={PLoS biology},
  volume={16},
  number={4},
  pages={e2004956},
  year={2018},
  publisher={Public Library of Science}
}

@book{dollar1999gender,
  title={Gender inequality, income, and growth: are good times good for women?},
  author={Dollar, David and Gatti, Roberta},
  volume={1},
  year={1999},
  publisher={Development Research Group, The World Bank Washington, DC}
}

@article{hunter2007matplotlib,
  title={Matplotlib: A 2D graphics environment},
  author={Hunter, John D},
  journal={Computing in science \& engineering},
  volume={9},
  number={3},
  pages={90},
  year={2007},
  publisher={IEEE Computer Society}
}

@article{10.7717/peerj-cs.156,
 title = {Comparison and benchmark of name-to-gender inference services},
 author = {Santamaría, Lucía and Mihaljević, Helena},
 year = 2018,
 month = jul,
 keywords = {Name-based gender inference, Classification algorithms, Performance evaluation, Gender analysis, Scientometrics, Bibliometrics},
 abstract = {
            The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to large databases of names, often enriched with information from social media profiles, culture-specific rules, and insights from sociolinguistics. We compare and benchmark five name-to-gender inference services by applying them to the classification of a test data set consisting of 7,076 manually labeled names. The compiled names are analyzed and characterized according to their geographical and cultural origin. We define a series of performance metrics to quantify various types of classification errors, and define a parameter tuning procedure to search for optimal values of the services’ free parameters. Finally, we perform benchmarks of all services under study regarding several scenarios where a particular metric is to be optimized.
         },
 volume = 4,
 pages = {e156},
 journal = {PeerJ Computer Science},
 issn = {2376-5992},
 url = {https://doi.org/10.7717/peerj-cs.156},
 doi = {10.7717/peerj-cs.156}
}

@article{Keyes:2018:MMT:3290265.3274357,
 author = {Keyes, Os},
 title = {The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition},
 journal = {Proc. ACM Hum.-Comput. Interact.},
 issue_date = {November 2018},
 volume = {2},
 number = {CSCW},
 month = nov,
 year = {2018},
 issn = {2573-0142},
 pages = {88:1--88:22},
 articleno = {88},
 numpages = {22},
 url = {http://doi.acm.org/10.1145/3274357},
 doi = {10.1145/3274357},
 acmid = {3274357},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {automatic gender recognition, gender, machine learning, transgender},
}

@article{CHENG201178,
title = "Author gender identification from text",
journal = "Digital Investigation",
volume = "8",
number = "1",
pages = "78 - 88",
year = "2011",
issn = "1742-2876",
doi = "https://doi.org/10.1016/j.diin.2011.04.002",
url = "http://www.sciencedirect.com/science/article/pii/S1742287611000247",
author = "Na Cheng and R. Chandramouli and K.P. Subbalakshmi",
keywords = "Gender identification, Text mining, Psycho-linguistic analysis, Logistic regression, Decision tree, Support vector machine",
abstract = "Text is still the most prevalent Internet media type. Examples of this include popular social networking applications such as Twitter, Craigslist, Facebook, etc. Other web applications such as e-mail, blog, chat rooms, etc. are also mostly text based. A question we address in this paper that deals with text based Internet forensics is the following: given a short text document, can we identify if the author is a man or a woman? This question is motivated by recent events where people faked their gender on the Internet. Note that this is different from the authorship attribution problem. In this paper we investigate author gender identification for short length, multi-genre, content-free text, such as the ones found in many Internet applications. Fundamental questions we ask are: do men and women inherently use different classes of language styles? If this is true, what are good linguistic features that indicate gender? Based on research in human psychology, we propose 545 psycho-linguistic and gender-preferential cues along with stylometric features to build the feature space for this identification problem. Note that identifying the correct set of features that indicate gender is an open research problem. Three machine learning algorithms (support vector machine, Bayesian logistic regression and AdaBoost decision tree) are then designed for gender identification based on the proposed features. Extensive experiments on large text corpora (Reuters Corpus Volume 1 newsgroup data and Enron e-mail data) indicate an accuracy up to 85.1% in identifying the gender. Experiments also indicate that function words, word-based features and structural features are significant gender discriminators."
}

@article{santamaria2018comparison,
  title={Comparison and benchmark of name-to-gender inference services},
  author={Santamar{\'\i}a, Luc{\'\i}a and Mihaljevi{\'c}, Helena},
  journal={PeerJ Computer Science},
  volume=4,
  pages={e156},
  year=2018,
  publisher={PeerJ Inc.}
}

@article{DBLP:journals/corr/KarimiWLJS16,
  author    = {Fariba Karimi and
               Claudia Wagner and
               Florian Lemmerich and
               Mohsen Jadidi and
               Markus Strohmaier},
  title     = {Inferring Gender from Names on the Web: {A} Comparative Evaluation
               of Gender Detection Methods},
  journal   = {CoRR},
  volume    = {abs/1603.04322},
  year      = {2016},
  url       = {http://arxiv.org/abs/1603.04322},
  archivePrefix = {arXiv},
  eprint    = {1603.04322},
  timestamp = {Mon, 13 Aug 2018 16:48:22 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/KarimiWLJS16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings {199317,
	author = {Mart{\'\i}n Abadi and Paul Barham and Jianmin Chen and Zhifeng Chen and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay Ghemawat and Geoffrey Irving and Michael Isard and Manjunath Kudlur and Josh Levenberg and Rajat Monga and Sherry Moore and Derek G. Murray and Benoit Steiner and Paul Tucker and Vijay Vasudevan and Pete Warden and Martin Wicke and Yuan Yu and Xiaoqiang Zheng},
	title = {TensorFlow: A System for Large-Scale Machine Learning},
	booktitle = {12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)},
	year = 2016,
	isbn = {978-1-931971-33-1},
	address = {Savannah, GA},
	pages = {265--283},
	url = {https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi},
	publisher = {{USENIX} Association},
}


@article{pedregosa2011scikit,
  title={Scikit-learn: Machine learning in Python},
  author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
  journal={Journal of machine learning research},
  volume=12,
  number={Oct},
  pages={2825--2830},
  year=2011
}

@article{loper2002nltk,
  title={NLTK: the natural language toolkit},
  author={Loper, Edward and Bird, Steven},
  journal={arXiv preprint cs/0205028},
  year=2002
}

@article{van2011numpy,
  title={The NumPy array: a structure for efficient numerical computation},
  author={Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael},
  journal={Computing in Science \& Engineering},
  volume=13,
  number=2,
  pages=22,
  year=2011,
  publisher={IEEE Computer Society}
}

@inproceedings{duenas2018perceval,
  title={Perceval: Software project data at your will},
  author={Due{\~n}as, Santiago and Cosentino, Valerio and Robles, Gregorio and Gonzalez-Barahona, Jesus M},
  booktitle={Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings},
  pages={1--4},
  year=2018,
  organization={ACM}
}

@article{hill2013wikipedia,
  title={The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation},
  author={Hill, Benjamin Mako and Shaw, Aaron},
  journal={PloS one},
  volume={8},
  number={6},
  pages={e65782},
  year={2013},
  publisher={Public Library of Science}
}

@inproceedings{antin2011gender,
  title={Gender differences in Wikipedia editing},
  author={Antin, Judd and Yee, Raymond and Cheshire, Coye and Nov, Oded},
  booktitle={Proceedings of the 7th international symposium on wikis and open collaboration},
  pages={11--14},
  year={2011},
  organization={ACM}
}

@inproceedings{robles2014floss,
  title={FLOSS 2013: A survey dataset about free software contributors: challenges for curating, sharing, and combining},
  author={Robles, Gregorio and Arjona Reina, Laura and Serebrenik, Alexander and Vasilescu, Bogdan and Gonz{\'a}lez-Barahona, Jes{\'u}s M},
  booktitle={Proceedings of the 11th Working Conference on Mining Software Repositories},
  pages={396--399},
  year={2014},
  organization={ACM}
}

@inproceedings{10.1007/978-3-319-39225-7_13,
author="Robles, Gregorio
and Reina, Laura Arjona
and Gonz{\'a}lez-Barahona, Jes{\'u}s M.
and Dom{\'i}nguez, Santiago Due{\~{n}}as",
editor="Crowston, Kevin
and Hammouda, Imed
and Lundell, Bj{\"o}rn
and Robles, Gregorio
and Gamalielsson, Jonas
and Lindman, Juho",
title="Women in Free/Libre/Open Source Software: The Situation in the 2010s",
booktitle="Open Source Systems: Integrating Communities",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="163--173",
abstract="Women are underrepresented in the IT sector. But the situation in FLOSS (free, libre, open source software) development is really extreme in this respect: past publications and studies show a female participation of around 2 {\%} to 5 {\%} and have shed some light into this problem. In this paper, we give an update the state of knowledge to the current situation of gender in FLOSS, by analyzing the results of surveying more than 2,000 contributors to FLOSS projects in 2013, of which more than 200 were women. Our findings confirm that women enter the FLOSS community later than men, do primarily other tasks than coding, participate less if they have children, and have slightly different reasons to enter (and to stay in) the development communities they join. However, we also find evidence that women are joining FLOSS projects in higher numbers in recent years, and that the share of women devoting few hours per week to FLOSS and full-time dedication is higher than for men. All in all, comparing our results with the ones from the 2000s, the context of participation of women in FLOSS has not changed much.",
isbn="978-3-319-39225-7"
}

@misc{krawetz2006gender,
  title={Gender Guesser},
  author={Krawetz, N},
  year={2006},
  publisher={Hacker Factor Solutions. http://www. hackerfactor. com/Gender-Guesser. html}
}

@inproceedings{mislove2011understanding,
  title={Understanding the demographics of twitter users},
  author={Mislove, Alan and Lehmann, Sune and Ahn, Yong-Yeol and Onnela, Jukka-Pekka and Rosenquist, J Niels},
  booktitle={Fifth international AAAI conference on weblogs and social media},
  year={2011}
}

@inproceedings{burger2011discriminating,
  title={Discriminating gender on Twitter},
  author={Burger, John D and Henderson, John and Kim, George and Zarrella, Guido},
  booktitle={Proceedings of the conference on empirical methods in natural language processing},
  pages={1301--1309},
  year={2011},
  organization={Association for Computational Linguistics}
}

@inproceedings{vasilescu2012gender,
  title={Gender, representation and online participation: A quantitative study of stackoverflow},
  author={Vasilescu, Bogdan and Capiluppi, Andrea and Serebrenik, Alexander},
  booktitle={2012 International Conference on Social Informatics},
  pages={332--338},
  year={2012},
  organization={IEEE}
}

@inproceedings{vasilescu2015gender,
  title={Gender and tenure diversity in GitHub teams},
  author={Vasilescu, Bogdan and Posnett, Daryl and Ray, Baishakhi and van den Brand, Mark GJ and Serebrenik, Alexander and Devanbu, Premkumar and Filkov, Vladimir},
  booktitle={Proceedings of the 33rd annual ACM conference on human factors in computing systems},
  pages={3789--3798},
  year={2015},
  organization={ACM}
}

@article{shaw2002users,
  title={Users divided? Exploring the gender gap in Internet use},
  author={Shaw, Lindsay H and Gant, Larry M},
  journal={CyberPsychology \& Behavior},
  volume={5},
  number={6},
  pages={517--527},
  year={2002},
  publisher={Mary Ann Liebert, Inc.}
}

@article{ranjan2017hyperface,
  title={Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition},
  author={Ranjan, Rajeev and Patel, Vishal M and Chellappa, Rama},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={41},
  number={1},
  pages={121--135},
  year={2017},
  publisher={IEEE}
}

@article{liwicki2011automatic,
  title={Automatic gender detection using on-line and off-line information},
  author={Liwicki, Marcus and Schlapbach, Andreas and Bunke, Horst},
  journal={Pattern Analysis and Applications},
  volume={14},
  number={1},
  pages={87--92},
  year={2011},
  publisher={Springer}
}

@article{berners2001semantic,
  title={The semantic web},
  author={Berners-Lee, Tim and Hendler, James and Lassila, Ora and others},
  journal={Scientific american},
  volume={284},
  number={5},
  pages={28--37},
  year={2001},
  publisher={New York, NY, USA:}
}

@article{42240,
  title = {Wikidata: A Free Collaborative Knowledge Base},
  author = {Denny Vrandečić and Markus Krötzsch},
  year = {2014},
  URL = {http://cacm.acm.org/magazines/2014/10/178785-wikidata/fulltext},
  journal = {Communications of the ACM},
  pages = {78--85},
  volume = {57}
}


@misc{giles2005internet,
  title={Internet encyclopaedias go head to head},
  author={Giles, Jim},
  year={2005},
  publisher={Nature Publishing Group}
}

@incollection{auer2007dbpedia,
  title={Dbpedia: A nucleus for a web of open data},
  author={Auer, S{\"o}ren and Bizer, Christian and Kobilarov, Georgi and Lehmann, Jens and Cyganiak, Richard and Ives, Zachary},
  booktitle={The semantic web},
  pages={722--735},
  year={2007},
  publisher={Springer}
}

@article{janssen2012benefits,
  title={Benefits, adoption barriers and myths of open data and open government},
  author={Janssen, Marijn and Charalabidis, Yannis and Zuiderwijk, Anneke},
  journal={Information systems management},
  volume={29},
  number={4},
  pages={258--268},
  year={2012},
  publisher={Taylor \& Francis}
}

@article{koppel2002automatically,
  title={Automatically categorizing written texts by author gender},
  author={Koppel, Moshe and Argamon, Shlomo and Shimoni, Anat Rachel},
  journal={Literary and linguistic computing},
  volume={17},
  number={4},
  pages={401--412},
  year={2002},
  publisher={Oxford University Press}
}

@article{janssen2012benefits,
  title={Benefits, adoption barriers and myths of open data and open government},
  author={Janssen, Marijn and Charalabidis, Yannis and Zuiderwijk, Anneke},
  journal={Information systems management},
  volume={29},
  number={4},
  pages={258--268},
  year={2012},
  publisher={Taylor \& Francis}
}

@techreport{ISO5725,
 author = {ISO},
 url = {https://www.iso.org/obp/ui/#iso:std:iso:5725:-1:ed-1:v1:en},
 Institution = {International Organization for Standardization},
 address = {Geneva, Switzerland},
 Title = {Accuracy (trueness and precision) of measurement methods and results — Part 1: General principles and definitions},
 number = {5725-1:1994},
 Type = {ISO},
 Year = {1994},
 }